使用Quanteda进行单词聚类

问题描述

有关数据结构和dfm：

library(quanteda)
 dataset1 <- data.frame( anumber = c(1,2,3,4,5,6),text = c("Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s,when an unkNown printer took a galley of type and scrambled it to make a type specimen book.","It has survived not only five centuries,but also the leap into electronic typesetting,remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages,and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum","Contrary to popular belief,Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC,making it over 2000 years old. Richard Mcclintock,a Latin professor at Hampden-Sydney College in Virginia,looked up one of the more obscure Latin words,consectetur,from a Lorem Ipsum passage,and going through the cites of the word in classical literature,discovered the undoubtable source.","Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s,discovered the undoubtable source."))
    myDfm <- dataset1$text %>%
        corpus() %>%                    
        tokens(remove_punct = TRUE,remove_numbers = TRUE,remove_symbols = TRUE) %>%
  
        dfm()

是否可以选择使用平均链接层次聚类算法创建具有jaccard相似度的相似度矩阵来确定紧靠在一起的单词之间的关联的层级聚类？要计算一个群集的每个单词到另一个群集的其他短语之间的平均距离，请使用未加权对组 均值平均法

解决方法

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